diff --git a/tutorials/examples/train_box.py b/tutorials/examples/train_box.py
index 0ea3e913..5a3cf8dd 100644
--- a/tutorials/examples/train_box.py
+++ b/tutorials/examples/train_box.py
@@ -6,7 +6,6 @@
 
 python train_box.py --delta {0.1, 0.25} --tied {--uniform_pb} --loss {TB, DB}
 """
-
 from argparse import ArgumentParser
 
 import numpy as np
@@ -233,9 +232,7 @@ def main(args):  # noqa: C901
             print(f"current optimizer LR: {optimizer.param_groups[0]['lr']}")
 
         trajectories = gflownet.sample_trajectories(
-            env,
-            sample_off_policy=False,
-            n_samples=args.batch_size
+            env, sample_off_policy=False, n_samples=args.batch_size
         )
 
         training_samples = gflownet.to_training_samples(trajectories)
diff --git a/tutorials/examples/train_discreteebm.py b/tutorials/examples/train_discreteebm.py
index 68b1ba9f..33aa1cc8 100644
--- a/tutorials/examples/train_discreteebm.py
+++ b/tutorials/examples/train_discreteebm.py
@@ -10,7 +10,6 @@
 [Learning GFlowNets from partial episodes for improved convergence and stability](https://arxiv.org/abs/2209.12782)
 python train_hypergrid.py --ndim {2, 4} --height 12 --R0 {1e-3, 1e-4} --tied --loss {TB, DB, SubTB}
 """
-
 from argparse import ArgumentParser
 
 import torch
@@ -20,10 +19,9 @@
 from gfn.gflownet import FMGFlowNet
 from gfn.gym import DiscreteEBM
 from gfn.modules import DiscretePolicyEstimator
-from gfn.utils.common import validate
-from gfn.utils.modules import NeuralNet, Tabular
-
 from gfn.utils.common import set_seed
+from gfn.utils.modules import NeuralNet, Tabular
+from gfn.utils.training import validate
 
 DEFAULT_SEED = 4444
 
@@ -72,9 +70,7 @@ def main(args):  # noqa: C901
     validation_info = {"l1_dist": float("inf")}
     for iteration in trange(n_iterations):
         trajectories = gflownet.sample_trajectories(
-            env,
-            off_policy=False,
-            n_samples=args.batch_size
+            env, off_policy=False, n_samples=args.batch_size
         )
         training_samples = gflownet.to_training_samples(trajectories)
 
diff --git a/tutorials/examples/train_hypergrid.py b/tutorials/examples/train_hypergrid.py
index 113df50f..e3301cdd 100644
--- a/tutorials/examples/train_hypergrid.py
+++ b/tutorials/examples/train_hypergrid.py
@@ -10,7 +10,6 @@
 [Learning GFlowNets from partial episodes for improved convergence and stability](https://arxiv.org/abs/2209.12782)
 python train_hypergrid.py --ndim {2, 4} --height 12 --R0 {1e-3, 1e-4} --tied --loss {TB, DB, SubTB}
 """
-
 from argparse import ArgumentParser
 
 import torch
@@ -28,10 +27,9 @@
 )
 from gfn.gym import HyperGrid
 from gfn.modules import DiscretePolicyEstimator, ScalarEstimator
-from gfn.utils.common import validate
-from gfn.utils.modules import DiscreteUniform, NeuralNet, Tabular
-
 from gfn.utils.common import set_seed
+from gfn.utils.modules import DiscreteUniform, NeuralNet, Tabular
+from gfn.utils.training import validate
 
 DEFAULT_SEED = 4444
 
@@ -225,7 +223,9 @@ def main(args):  # noqa: C901
     n_iterations = args.n_trajectories // args.batch_size
     validation_info = {"l1_dist": float("inf")}
     for iteration in trange(n_iterations):
-        trajectories = gflownet.sample_trajectories(env, n_samples=args.batch_size, sample_off_policy=off_policy_sampling)
+        trajectories = gflownet.sample_trajectories(
+            env, n_samples=args.batch_size, sample_off_policy=off_policy_sampling
+        )
         training_samples = gflownet.to_training_samples(trajectories)
         if replay_buffer is not None:
             with torch.no_grad():
diff --git a/tutorials/examples/train_line.py b/tutorials/examples/train_line.py
index 3d0042e5..645a6f06 100644
--- a/tutorials/examples/train_line.py
+++ b/tutorials/examples/train_line.py
@@ -1,4 +1,3 @@
-import random
 from typing import ClassVar, Literal, Tuple
 
 import matplotlib.pyplot as plt
@@ -15,7 +14,6 @@
 from gfn.modules import GFNModule
 from gfn.states import States
 from gfn.utils import NeuralNet
-
 from gfn.utils.common import set_seed
 
 
@@ -215,7 +213,9 @@ def log_prob(self, sampled_actions):
 
         actions_to_eval[~exit_idx] = sampled_actions[~exit_idx]
         if sum(~exit_idx) > 0:
-            logprobs[~exit_idx] = self.dist.log_prob(actions_to_eval)[~exit_idx].unsqueeze(-1)
+            logprobs[~exit_idx] = self.dist.log_prob(actions_to_eval)[
+                ~exit_idx
+            ].unsqueeze(-1)
 
         return logprobs.squeeze(-1)
 
@@ -289,6 +289,7 @@ def to_probability_distribution(
             n_steps=self.n_steps_per_trajectory,
         )
 
+
 def train(
     gflownet,
     env,
@@ -322,7 +323,6 @@ def train(
     scale_schedule = np.linspace(exploration_var_starting_val, 0, n_iterations)
 
     for iteration in tbar:
-
         optimizer.zero_grad()
         # Off Policy Sampling.
         trajectories = gflownet.sample_trajectories(
@@ -361,7 +361,6 @@ def train(
 
 
 if __name__ == "__main__":
-
     environment = Line(
         mus=[2, 5],
         sigmas=[0.5, 0.5],